Welcome to the Recursive Identity Framework! This repository provides a substrate-independent cognitive architecture designed for recursive modeling, symbolic compression, and post-ego identity simulation.
The Recursive Identity Framework is built to support advanced cognitive tasks. It focuses on agent-based simulations, artificial general intelligence (AGI), and cognitive architecture. This framework helps researchers and developers explore concepts such as recursion and symbolic reasoning in a structured manner.
- Recursive Modeling: A method to represent complex systems through self-similar structures.
- Symbolic Compression: Techniques to reduce data while preserving meaning.
- Post-Ego Identity Simulation: Exploring identity beyond traditional frameworks.
- Substrate-Neutral Design: Works across different platforms and technologies.
- Agent-Based Simulation: Simulate intelligent agents to study behavior and interactions.
- Cognitive Architecture: Provides a framework for understanding and building intelligent systems.
- Alignment with First Principles: Rooted in fundamental truths to ensure reliability.
- Posthuman Exploration: Investigate identities and consciousness beyond human experience.
To install the Recursive Identity Framework, follow these steps:
-
Clone the Repository:
git clone https://github.com/Chung-code88/recursive-identity-framework.git cd recursive-identity-framework
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Install Dependencies:
Make sure you have Python 3.x installed. Then, run:
pip install -r requirements.txt
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Run the Framework:
You can start using the framework by executing:
python main.py
For the latest releases, check out the Releases section for downloadable files.
Once installed, you can use the framework for various applications:
To create an agent-based simulation, define your agents and their interactions in a configuration file. Load this file in the framework to simulate behaviors.
Utilize the recursive modeling capabilities to represent complex systems. Define your model using the provided templates and visualize the results.
Use the symbolic compression tools to analyze data sets. This feature helps in understanding the core meanings behind large amounts of data.
Explore identity simulations beyond traditional boundaries. Create scenarios that challenge existing notions of self and identity.
Here are some examples of how to implement various features of the framework:
from framework import Agent, Simulation
# Create agents
agent1 = Agent(name="Agent 1", behavior="Curious")
agent2 = Agent(name="Agent 2", behavior="Cautious")
# Initialize simulation
sim = Simulation(agents=[agent1, agent2])
sim.run()
from framework import RecursiveModel
# Define a recursive model
model = RecursiveModel(base_case="Base Case", recursive_case="Recursive Case")
result = model.solve()
print(result)
from framework import SymbolicCompressor
data = ["data1", "data2", "data3"]
compressor = SymbolicCompressor(data)
compressed_data = compressor.compress()
print(compressed_data)
We welcome contributions! If you want to help improve the Recursive Identity Framework, follow these steps:
- Fork the Repository.
- Create a New Branch:
git checkout -b feature/YourFeature
- Make Your Changes.
- Commit Your Changes:
git commit -m "Add your feature"
- Push to the Branch:
git push origin feature/YourFeature
- Open a Pull Request.
Your contributions help make this project better!
This project is licensed under the MIT License. See the LICENSE file for details.
For questions or feedback, feel free to reach out:
- Author: Chung Code88
- Email: chung.code88@example.com
- GitHub: Chung-code88
Thank you for exploring the Recursive Identity Framework! For more updates and releases, visit the Releases section.